This paper evaluates the performance of Large Language Models (LLMs) in authorship attribu- tion and authorship verification tasks for Latin texts of the Patristic Era. The study showcases that LLMs can be robust in zero-shot author- ship verification even on short texts without sophisticated feature engineering. Yet, the mod- els can also be easily “mislead” by semantics. The experiments also demonstrate that steering the model’s authorship analysis and decision- making is challenging, unlike what is reported in the studies dealing with high-resource mod- ern languages. Although LLMs prove to be able to beat, under certain circumstances, the traditional baselines, obtaining a nuanced and truly explainable decision requires at best a lot of experimentation.